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PurposeLibrary Hi Tech is one of the most influential journals that publish leading research in library and information science (LIS). The present study aims to understand the scholarly communication in Library Hi Tech by profiling its historic footprint, emerging trends and knowledge diffusion.Design/methodology/approachA total of 3,131 bibliographic records between 1995 and 2018 were collected from the Web of Science. Text mining, graph analysis and data visualization were used to analyze subject category assignment, domain-level citation trends, co-occurrence of keywords, keyword bursts, networks of document co-citation and landmark articles.FindingsFindings indicated that published research in the journal was largely influenced by the psychology, education and social domain as a unidisciplinary discipline. Knowledge of the journal has been disseminated into multiple domains such as LIS, computer science and education. Dominant thematic concentrations were also identified: (1) library services in academic libraries and related to digital libraries, (2) adoption of new information technologies and (3) information-seeking behavior in these contexts. Additionally, the journal has exhibited an increased research emphasis on mixed-method user-centered studies and investigations into libraries' use of new media.Originality/valueThis study provides a promising approach to understand scientific trends and the intellectual growth of journals. It also helps Library Hi Tech to become more self-explanatory with a detailed bibliometric profile and to identify future directions in editorship and readership. Finally, researchers in the community can better position their studies within the emerging trends and current challenges of the journal.

Twelve evidence-based profiles of roles across the translational workforce and two patients were made available through clinical and translational science (CTS) Personas, a project of the Clinical and Translational Science Awards (CTSA) Program National Center for Data to Health (CD2H). The persona profiles were designed and researched to demonstrate the key responsibilities, motivators, goals, software use, pain points, and professional development needs of those working across the spectrum of translation, from basic science to clinical research to public health. The project’s goal was to provide reliable documents that could be used to inform CTSA software development projects, educational resources, and communication initiatives. This paper presents the initiative to create personas for the translational workforce, including the methodology, engagement strategy, and lessons learned. Challenges faced and successes achieved by the project may serve as a roadmap for others searching for best practices in the creation of Persona profiles.

Objective The Unified Medical Language System (UMLS) is 1 of the most successful, collaborative efforts of terminology resource development in biomedicine. The present study aims to 1) survey historical footprints, emerging technologies, and the existing challenges in the use of UMLS resources and tools, and 2) present potential future directions. Materials and Methods We collected 10 469 bibliographic records published between 1986 and 2019, using a Web of Science database. graph analysis, data visualization, and text mining to analyze domain-level citations, subject categories, keyword co-occurrence and bursts, document co-citation networks, and landmark papers. Results The findings show that the development of UMLS resources and tools have been led by interdisciplinary collaboration among medicine, biology, and computer science. Efforts encompassing multiple disciplines, such as medical informatics, biochemical sciences, and genetics, were the driving forces behind the domain’s growth. The following topics were found to be the dominant research themes from the early phases to mid-phases: 1) development and extension of ontologies and 2) enhancing the integrity and accessibility of these resources. Knowledge discovery using machine learning and natural language processing and applications in broader contexts such as drug safety surveillance have recently been receiving increasing attention. Discussion Our analysis confirms that while reaching its scientific maturity, UMLS research aims to boundary-span to more variety in the biomedical context. We also made some recommendations for editorship and authorship in the domain. Conclusion The present study provides a systematic approach to map the intellectual growth of science, as well as a self-explanatory bibliometric profile of the published UMLS literature. It also suggests potential future directions. Using the findings of this study, the scientific community can better align the studies within the emerging agenda and current challenges.

The Pediatric Brain Tumor Atlas (PBTA), created as a multi-center effort by the Children’s Brain Tumor Tissue Consortium (CBTTC) and Pacific Pediatric Neuro-Oncology Consortium (PNOC) and supported by the Gabriella Miller Kids First Data Resource Center (DRC), has the initial goal of comprehensively characterizing over 1,600 clinically annotated pediatric brain tumor samples and making the data publicly available to cancer researchers worldwide in near real time and without embargo. PBTA comprises comprehensive clinical data in addition to whole exome sequencing, whole genome sequencing (WGS), RNA sequencing (RNASeq), miRNA sequencing, and proteomics. To address the need to inform novel discovery and clinical implementation of genomic approaches for diagnostic/therapeutic purposes, PBTA utilizes the cloud-based research environment of the Gabriella Miller Kids First DRC to provide near real-time integration, dissemination, processing, and sharing of associated petabyte-scale harmonized data. The PBTA initiative leverages the DRC platform’s users to access their dbGAP approved use of TARGET and other pediatric cancer datasets hosted by NCI’s Cancer Genomics’ Cloud and Genomic Data Commons, allowing for cross-disease studies. Processed annotations is further enabled for the PBTA via PedcBioPortal, a data visualization/analysis application further integrating additional public and deposited datasets. In its first data release are over 30 different types of pediatric brain tumors representing over 1,000 subjects. This data is available on the Kids First DRC Portal and PedcBioPortal and users can seamlessly move between applications. Data types include those for matched tumor/normal samples, such as WGS, RNASeq, proteomics, longitudinal clinical data, imaging data (MRIs and radiology reports), histology slide images, and pathology reports. CBTTC/PNOC promote real-time data release and collaborative discovery via shared resources. The combination of cloud-based analytic platforms with data from both genomics and clinical practice serves to define a new paradigm for accelerated pediatric cancer translational research.

Web development is a learning context with the potential to support rich computational thinking. Large-scale analysis of compilation and runtime errors have been used in introductory programming courses and similar approaches can be used to understand learning in web development environments. We investigated activity logs of a novel web coding game to uncover learning trajectories and what people struggle with when learning flexible box (flexbox), a collection of new CSS layout features. We designed a game called Flexbox Froggy, in which learners solve challenges by writing a few lines of CSS code, moving from simple levels that require knowledge of one flexbox property, to complex levels combining multiple properties. We investigate learning curves based on the changes in syntactic and semantic errors learners make as they complete the game. Our findings show that people performed better encountering a single new property than combined with properties they had already practiced. Clusters of learners at different levels did not demonstrate expected error rates based on learning curve theory. Also unexpectedly, advanced groups that mastered syntax had higher semantic error rates than the beginner group, especially when attempting new properties or complex use cases. We conclude with implications for designing and developing introductory web programming games and other instructional materials.

Information visualization and data visualization are often viewed as similar, but distinct domains, and they have drawn an increasingly broad range of interest from diverse sectors of academia and industry. This study systematically analyzes and compares the intellectual landscapes of the two domains between 2000 and 2014. The present study is based on bibliographic records retrieved from the Web of Science. Using a topic search and a citation expansion, we collected two sets of data in each domain. Then, we identified emerging trends and recent developments in information visualization and data visualization, captivated in intellectual landscapes, landmark articles, bursting keywords, and citation trends of the domains. We found out that both domains have computer engineering and applications as their shared grounds. Our study reveals that information visualization and data visualization have scrutinized algorithmic concepts underlying the domains in their early years. Successive literature citing the datasets focuses on applying information and data visualization techniques to biomedical research. Recent thematic trends in the fields reflect that they are also diverging from each other. In data visualization, emerging topics and new developments cover dimensionality reduction and applications of visual techniques to genomics. Information visualization research is scrutinizing cognitive and theoretical aspects. In conclusion, information visualization and data visualization have co-evolved. At the same time, both fields are distinctively developing with their own scientific interests.

Our previous scientometric review of regenerative medicine provides a snapshot of the fast-growing field up to the end of 2011. The new review identifies emerging trends and new developments appearing in the literature of regenerative medicine based on relevant articles and reviews published between 2000 and the first month of 2014.Multiple datasets of publications relevant to regenerative medicine are constructed through topic search and citation expansion to ensure adequate coverage of the field. Networks of co-cited references representing the literature of regenerative medicine are constructed and visualized based on a combined dataset of 71,393 articles published between 2000 and 2014. Structural and temporal dynamics are identified in terms of most active topical areas and cited references. New developments are identified in terms of newly emerged clusters and research areas. Disciplinary-level patterns are visualized in dual-map overlays.While research in induced pluripotent stem cells remains the most prominent area in the field of regenerative medicine, research related to clinical and therapeutic applications in regenerative medicine has experienced a considerable growth. In addition, clinical and therapeutic developments in regenerative medicine have demonstrated profound connections with the induced pluripotent stem cell research and stem cell research in general. A rapid adaptation of graphene-based nanomaterials in regenerative medicine is evident. Both basic research represented by stem cell research and application-oriented research typically found in tissue engineering are now increasingly integrated in the scientometric landscape of regenerative medicine. Tissue engineering is an interdisciplinary field in its own right. Advances in multiple disciplines such as stem cell research and graphene research have strengthened the connections between tissue engineering and regenerative medicine.

Introduction: The literature of orphan drugs and rare diseases between 2000 and 2014 is reviewed. The overall structure of its intellectual landscape is characterized in terms of thematic concentra...

Social media is changing existing information behavior by giving users access to real-time online information channels without the constraints of time and space. Social media, therefore, has created an enormous data analysis challenge for scientists trying to keep pace with developments in their field. Most previous studies have adopted broad-brush approaches that typically result in limited analysis possibilities. To address this problem, we applied text-mining techniques to Twitter data related to the 2012 Korean presidential election. We use three primary techniques: topic modeling to track changes in topical trends, mention-direction-based user network analysis, and term co-occurrence retrieval for further content analysis. Our study reveals that Twitter could be a useful way to detect and trace the advent of and changes in social issues, while analyzing mention-based user networks could show different aspects of user behaviors.

The advent of Web 2.0 and social media is taking a leading role of emerging big data. At the same time, however, informational dysfunction such as infri ngement of one's rights and violation of social order has been increasing sharply. This stu dy, therefore, aims at defining malicious usage, identifying malicious feature, and devising an automated method for classifying them. In particular, the rule-based experiment reveals statisti cally significant performance enhancement. 키워드 : 소셜미디어 마이닝 , 트위터 , 악의적 이용 , 자동분류 Social Media Mining, Twitter, Malicious Usage, Automatic Classification ********************* 본 연구는 2012년 정부(교육과학기술부)의 재원으로 한국연구재단 한국사회과학연구지원사업(SSK)의 지원을 받아 수행된 것임 (NRF-2012-2012S1A3A2033291).연세대학교 문헌정보학과 대학원(andrewevans@yonsei.ac.kr)연세대학교 문헌정보학과 석사과정(moomyungz@naver.com)연세대학교 문헌정보학과 학사과정(hng88@naver.com)연세대학교 문헌정보학과 석사과정(arcoiris@yonsei.ac.kr)연세대학교 문헌정보학과 부교수(min.song@yonsei.ac.kr)논문접수일자: 2013년 1월 15일 최초심사일자 : 2013년 1월 21일 게재확정일자 : 2013년 2월 2일한국문헌정보학회지, 47(1): 269-286, 2013. [http ://dx.doi.org/10. 4275/KSLIS.2013.47.1.269]

We describe the design rationale and principles of a web authoring tool for beginner web developers called Snowball. We explain how this constructionist toolkit exposes content creators to computational features of web pages, enabling them to create meaningful artifacts while they move from content creation to basic coding. Finally, we discuss how we are instrumenting learning analytics in Snowball.

Patients are facing complex health care decisions everyday. They are given many different treatment paths and often do not understand or question if these are the best path for their health conditions. These patients often turn to the internet, including authorative healthcare web sites and social media, to explore which option is the best for them [1][2]. In their exploration of different treatment options, however, they often notice gaps in research. Much literature supports these dissatisfactory experiences of healthcare consumers [4][6][7]. Therefore, less healthcare research reaches the benefiting stage. Eventually, although patients are concerned and interested in research, they often do not have a way to connect to researchers. To properly tackle this problematic issue, we propose an online platform called Act Together and Connect for patient-centered outcome research or ACTONNECT in short. ACTONNECT is based on the grounded theory of action-based matchmaking and engagement [3][5]. Meaning, if there are established actions from both partners, the matchmaking process is more likely to be successful. Based on previous research, there are three levels of engagements in the matchmaking process: 1) The initial willingness from both parties, one who is looking to be connected with the other to develop rigorous research studies, 2) The two groups are engaged by actions, physically sharing in the activities of creating research studies, protocol and publishing's, and 3) The Engaged partnership, which is literally the 1 +1 > 2 partnership. Based on this concept, ACTONNECT allows the matchmaking at the highest level by allowing patients and researchers who share a common ground. Hence, we view ACTONNECT as a common platform for both patients and researchers. With the action-based matchmaking, the partnership between patients and researchers can be more sustainable. To properly tackle this problematic issue, we propose an online platform called Act Together and Connect for patient-centered outcome research or ACTONNECT in short. ACTONNECT is based on the grounded theory of action-based matchmaking and engagement [3][5]. Meaning, if there are established actions from both partners, the matchmaking process is more likely to be successful. Based on previous research, there are three levels of engagements in the matchmaking process: 1) The initial willingness from both parties, one who is looking to be connected with the other to develop rigorous research studies, 2) The two groups are engaged by actions, physically sharing in the activities of creating research studies, protocol and publishing's, and 3) The Engaged partnership, which is literally the 1 +1 > 2 partnership. Based on this concept, ACTONNECT allows the matchmaking at the highest level by allowing patients and researchers who share a common ground. Hence, we view ACTONNECT as a common platform for both patients and researchers. With the action-based matchmaking, the partnership between patients and researchers can be more sustainable. In order to facilitate dynamic interaction among various stakeholders around healthcare issues, ACTONNECT features following functions: 1) healthcare heterogeneous network visualization, 2) ACTONNECTION publisher, 3) ACTONNECTION matchmaker, and 4) research article recommender. Figure 1 illustrate the user interface of the system. First, stakeholders can explore the healthcare social media through investigating the extracted healthcare heterogeneous network and identifying related research articles form authoritative healthcare sites such as WebMD and Pub Med. A healthcare heterogeneous network [8] is a graph consisting of nodes and links, where. Each node belongs to one particular type from, such as Drug (R), Disease (D), Adverse Drug Reactionss (A), Treatment (T), Diagnostics (G), Users (U), etc. Each link belongs to one particular relation from, such as treat between T and D, cause between R and A, use between U and R, etc. We construct the healthcare heterogeneous network by extracting the healthcare related discussion threads in social media, annotating the messages, and computing the strength of association between the extracted entities. Users can click on the nodes and links to find relevant discussions on diseases, drugs, treatments, etc. In this work, the social media content is crawled from a healthcare social media site, MedHelp.org. Through researching the social media and authorative healthcare information, users find out the experience of other patients on various drug and treatment options as well as some other scientific findings. Patients and researchers are able to post their insights through pubishing their own ACTONNECTION in the system. The ACTONNECT system will recommend other Connections, based on the document similarity. Hence, users view the insights of other patients, caregivers, physicians and researchers. Users may connect to each other to discuss their findings and establish collaborations of new research problems.

The advent of social media is changing the existing information behavior by letting users access to real-time online information channels without the constraints of time and space. It also generates a huge amount of data worth discovering novel knowledge. Social media, therefore, has created an enormous challenge for scientists trying to keep pace with developments in their field. Most of the previous studies have adopted broad-brush approaches which tend to result in providing limited analysis. To handle these problems properly, we introduce our real-time Twitter trend mining system, RT2M, which operates in real-time to process big stream datasets available on Twitter. The system offers the functions of term co-occurrence retrieval, visualization of Twitter users by query, similarity calculation between two users, Topic Modeling to keep track of changes of topical trend, and analysis on mention-based user networks. We also demonstrate an empirical study on 2012 Korean presidential election. The case study reveals Twitter could be a useful source to detect and predict the advent and changes of social issues, and analysis of mention-based user networks could show different aspects of user behaviors.